Color Image Segmentation Mentor : Dr. Rajeev Srivastava Students: Achit Kumar Ojha Aseem Kumar Akshay Tyagi

Segmentation Definition A process of separating of individual perception of the scene. Importance The first step in image processing.

Segmentation Techniques Clustering Approach Groups the pixel into clusters such that the variance is minimized Histogram Based Histogram is created from the pixels of the image and clusters are located from the valleys in the histogram Region-based approach –Region growing –Region splitting –Region merging –Their combination

K Means Clustering Algorithm Pick K cluster centers, either randomly or based on some heuristic. 2. Assign each pixel in the image to the cluster that minimizes the variance between cluster center and the pixel. 3. Re-compute the clusters centers by averaging all of the pixels in the cluster. 4. Repeat Steps 2 & 3 till no pixel changes cluster.

Problems with simple K-Means in segmetation Sensitive to initializations Can be applied to homogeneous textured image only because there is no local connection between data points and its neighbors.

Steps involved Original Image Diffusion based filtering Dominant color extration Spatial K- Means Segmented Image

Diffusion Based Filtering Anisotropic Diffusion as described by Perona & Malik Suitable as it preserves the important information in image The recursive iteration forumula is I x,y t+1 = I x,y t + λ Σ (D ( ∇ j I) ∇ j I )

Dominant Color Extraction 1. Contruct the histograms for each channel. 2. Partition each histogram in R sections. 3. Compute the peaks in each section and rank the peaks p1, p2, p where p1 has the highest number of elements.

Dominant Color Extraction 4. Start to form the color seeds with the highest peak pi if ( pi is red ) mark the pixels in the red channel and calculate the green mean and blue mean for the marked pixels. if ( pi is green) mark the pixels in the green channel and calculate the red mean and blue mean for the marked pixels. If ( pi is blue) mark the pixels in the blue channel and calculate the red mean and green mean for the marked pixels. 5. Form the color seed and eliminate pi from the list 6. Repeat the steps 4 and 5 until the desired number of color seeds has been reached.

Spatial K-Means Algorithm Original objective function of KMeans is J= Σ Σ || xi(j) - cj || Two more descriptors are used that sample local color homogenity and local texture complexity.

Sample Outputs Original Image Segmented Image ( 5 clusters)

Sample Output Original ImageSegmented Image( 5 cluster)

Improvements Use of Kohenen maps to determine the optimal number of clusters and cluster center Use of adaptive diffusion method.

Automatic seed growing technique Overview of the algorithm: 1 ) The input RGB color image is transformed into YUV color space. 2 ) Initial seeds are automatically selected. 3 ) The color image is segmented into regions where each region corresponds to a seed or group of connected seeds.

Steps involved in the process Transform the color space from RGB to YUV Apply the automated seed generation algorithm to obtain seed for seeded region growing Apply the region growing to obtain final segmented image

Converting from RGB to YUV The above relation can be shown in form of simple equations : Y =(0.257 * R) + (0.504 * G) + (0.098 * B) + 16 V = (0.439 * R) - (0.368 * G) - (0.071 * B) U =-(0.148 * R) - (0.291 * G) + (0.439 * B) For converting back from YUV to RGB the relation can be derived from above equations.

Automatic seed selection For automatic seed selection, the following three criteria must be satisfied: the seed pixel must have high similarity to its neighbors. for an expected region, at least one seed must be generated in order to produce this region. seeds for different regions must be disconnected. Condition 1. A seed pixel candidate must have the similarity higher than a threshold value Condition 2. A seed pixel candidate must have the maximum relative Euclidean distance to its eight neighbors less than a threshold value.

Region growing Assign a label to each seed region. Record neighbors of all regions in a sorted list T in decreasing order of distances. While T is not empty, remove the first point p and check its 4- neighbors. If all labeled neighbors of p have a same label, set p to this label. If the labeled neighbors of p have different labels, calculate the distances between p and all neighboring regions and classify p to the nearest region. Then update the mean of this region, and add 4- neighbors of p, which are neither classified yet nor in T, to T in a decreasing order of distances.

Sample Output

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